Dimensionality Reduction Using a Novel Neural Network Based Feature Extraction Method

Abstract

A novel neural network based method for feature extraction is proposed. The method achieves dimensionality reduction of input vectors used for supervised learning problems. Combinations of the original features are formed that maximize the sensitivity of the network's outputs with respect to variations of its inputs. The method exhibits some similarity to Principal Component Analysis, but also takes into account supervised character of the learning task. It is applied to classification problems leading to efficient dimensionality reduction and increased generalization ability. 2. Introduction Methods for dimensionality reduction concentrate either on selecting from the original set of features a smaller subset of salient features, or on combining the original features in such a way as to extract a small number of salient features. Application of such methods to data analysis or pattern recognition problems has distinct advantages in terms of generalization properties and processing s..